具有多传输通道系统的网络化预测控制

本文研究了一类具有多传输通道网络化系统的控制问题,基于网络化预测控制方法,提出了一种改进型的分布式预测补偿方式,从而更有效地利用反馈数据来提高控制系统的性能.对闭环网络化预测控制系统进行分析,得到其稳定性条件,特别地,在模型精确已知和多传输通道的时延为定常的情况下,该条件将会退化为本地控制的闭环系统稳定性条件.上述结论的好处是网络化预测控制系统中状态观测器和控制器的设计可以参考本地控制.通过球杆系统算例验证本文所提方法的正确性和有效性.     

异构多无人机网络化协同控制系统的可控性

研究具有Leader-Follower结构和分布式通信拓扑的异构多无人机网络化分布式协同控制系统的可控性问题. 基于同构网络的受控一致性思想建立了异构多飞行器网络控制系统的动态模型; 并针对该动态模型的不同形式, 基于代数图论和传统的控制理论, 分别得到了异构多无人机网络化协同控制系统的可控性条件, 尤其是可控性与该网络化系统中通信拓扑之间的关系; 然后分析且提出了改善系统可控性的可行性方法. 最后仿真结果验证了本文相关结论的正确性.     

多速率分布式预测控制及其在热连轧活套系统中的应用

多智能体协调控制系统更适合采用分布式控制方式,但是处理智能体之间的耦合影响是分布式控制的一个难点.本文针对串联结构下的多智能体系统,提出一类多速率分布式预测控制策略,异步更新多智能体的控制律,能够充分考虑智能体之间的耦合影响,提高系统的稳定性,并给出了系统稳定的充分条件.最后,将多速率分布式控制算法应用到热连轧活套系统,仿真验证了方法的有效性和可行性.     

高温环境模拟试验测控系统设计及高温控制方法研究

高温环境模拟试验系统环境控制段采用现场级、下位控制级、上位管理级的分布式测控结构的设计方式,产品试验段采用总线式的采集方式;由于目标温度高、燃烧过程复杂、各环境参数之间关联性强,对燃烧高温的控制设计了基于燃烧专家知识推理和智能控制器的多回路双闭环控制系统;以分布式控制系统为基础,融合燃烧专家知识并加以优化,根据不同的试验工况和温度偏差归纳出高温控制专家系统的推理规则;实际应用中能达到很好控制指标,对类似的燃烧高温控制有一定借鉴意义.     

铝锭浇铸系统两级控制方法的研究

在分析铝锭浇铸过程特点的基础上,借鉴串级控制的思想,提出了一种两级控制的策略,即将控制系统分为两级,简化了系统结构.系统中的第一级控制回路稳定控制溜槽铝液的流量,第二级控制回路精确控制铝锭的浇铸;同时根据一二级回路的各自特点,分别采用PID控制和模糊PID控制作为两个控制回路的算法,并通过仿真对所提出的控制方法进行了验证.     

并联系统分布预测控制

并联结构的过程网络控制系统存在精确建模困难、算法计算时间长的问题。如果缩短计算时间,则会导致控制精度不足,为此提出了一种分布式预测控制算法。将并联系统分解为多个子系统,每个子系统通过相互协作、迭代计算,完成整个并联系统的控制。并联系统存在独特的竞争性耦合形式。通过定义并联系统竞争性耦合结构,完成分布式预测控制算法的高效迭代,有效减少了计算量。优化过程中考虑了竞争性约束。在保证每个子系统最优的前提下,通过运行算法给出的迭代计算步骤,达到系统整体最优,实现了低成本在线实时优化与控制的目标。以燃气锅炉供暖系统为例,利用MATLAB对分布式预测控制算法进行仿真研究,并将其与集中式预测控制算法仿真结果进行对比。结果验证了该算法的有效性和实用性。     

多回路网络控制系统中调度与控制的协同设计方法

结合具有多控制回路的网络控制系统框架结构,设计了一种基于网络控制性能运行状态的调度器的算法,根据系统对控制性能的要求,通过实时调整各控制回路的采样周期,实现多控制回路NCS的系统性能优化,并通过一组仿真给予验证.仿真结果显示,所设计的调度器能够对多控制回路的网络控制系统的有限资源进行有效的调度,提高资源利用率.     

航空发动机分布式控制系统原理样机研制

分布式控制是航空发动机控制发展的重要方向之一,目前国内主要在智能装置的研制方面取得了一定的进展,尚未研制出航空发动机分布式控制系统的原理样机;首先就基于CAN总线的航空发动机分布式控制系统设计了总体框架,然后就系统构建中的关键技术,如CAN总线数据传输、控制系统的同步等问题作了详细阐述;最终在实验室条件下成功构建了原理样机,进行了分布式控制系统的在回路仿真验证。     

资源受限的网络控制系统调度

结合具有多控制回路的网络控制系统框架结构,设计了反馈调度器以实现调度策略.根据控制性能最优化要求,分析了系统的可调度性.通过实时调整各控制回路的采样周期,采用优先级分配方法,在线优化多控制回路的网络控制系统性能.通过对3个控制回路组成的网络控制系统进行仿真实验,验证了可调度分析和调度策略的有效性.     

液位控制系统中混沌运动的小脑模型控制

一般的控制系统可能会因为一些非线性故障转变为混沌动力学系统.液位控制系统中的比例反馈回路中存在非线性环节,使液位控制系统的运动成为混沌运动.针对这种情况下的混沌数学模型,通过仿真验证小脑模型神经元网络控制方法,仿真图说明控制方法对混沌模型进行有效控制,可以将稳态值控制到与期望值一致.同时小脑模型神经元网络具有计算速度快的优点,这种控制方法在实际工程领域中的应用值得研究.     

Distributed model predictive control of an experimental four-tank system

A distributed model predictive control (DMPC) framework is proposed. The physical plant structure and the plant mathematical model are used to partition the system into self-sufficient estimation and control nodes. Local measurements at the nodes are used to estimate the relevant plant states. This information is then used in the model predictive control calculations. Communication among relevant nodes during estimation and control calculations provides improvement over the performance of completely decentralized controllers. The DMPC framework is demonstrated for the level control of an experimental four-tank system. The performance of the DMPC system for disturbance rejection is compared with other control configurations. The results indicate that the proposed framework provides significant improvement over completely decentralized MPC controllers, and approaches the performance of a fully centralized design.     

通讯信息约束下具有全局稳定性的分布式系统预测控制

本文针对一类由状态相互耦合的子系统组成的分布式系统, 提出了一种可以处理输入约束的保证稳定性的非 迭代协调分布式预测控制方法(distributed model predictive control, DMPC). 该方法中, 每个控制器在求解控制率时只与 其它控制器通信一次来满足系统对通信负荷限制; 同时, 通过优化全局性能指标来提高优化性能. 另外, 该方法在优化 问题中加入了一致性约束来限制关联子系统的估计状态与当前时刻更新的状态之间的偏差, 进而保证各子系统优化问 题初始可行时, 后续时刻相继可行. 在此基础上, 通过加入终端约束来保证闭环系统渐进稳定. 该方法能够在使用较少 的通信和计算负荷情况下, 提高系统优化性能. 即使对于强耦合系统同样能够保证优化问题的递推可行性和闭环系统的 渐进稳定性. 仿真结果验证了本文所提出方法的有效性.     

面向大规模网络化系统的分布式预测控制

本文将近年来关于网络化分布式预测控制(distributed model predictive contro, DMPC)设计的结果进行了总结 概述. DMPC不仅仅继承了预测控制的优点而且还有分布式控制框架的特点. 首先, 介绍了分布式控制的系统结构设计; 然后, 依据预测控制中的性能指标, 从3个方面对DMPC进行了介绍: 基于局部性能指标的DMPC, 基于邻域指标的 DMPC和基于全局指标的DMPC. 最后, 选取3个典型例子来说明一些DMPC的有效性.     

A robust distributed model predictive control algorithm

Although distributed model predictive control (DMPC) has received significant attention in the literature, the robustness of DMPC with respect to model errors has not been explicitly addressed. In this paper, a novel online algorithm that deals explicitly with model errors for DMPC is proposed. The algorithm requires decomposing the entire system into N subsystems and solving N convex optimization problems to minimize an upper bound on a robust performance objective by using a time-varying state-feedback controller for each subsystem. Simulations examples were considered to illustrate the application of the proposed method.     

Distributed model predictive control with hierarchical architecture for communication: application in automated irrigation channels

This paper is concerned with a distributed model predictive control (DMPC) method that is based on a distributed optimisation method with two-level architecture for communication. Feasibility (constraints satisfaction by the approximated solution), convergence and optimality of this distributed optimisation method are mathematically proved. For an automated irrigation channel, the satisfactory performance of the proposed DMPC method in attenuation of the undesired upstream transient error propagation and amplification phenomenon is illustrated and compared with the performance of another DMPC method that exploits a single-level architecture for communication. It is illustrated that the DMPC that exploits a two-level architecture for communication has a better performance by better managing communication overhead.     

Handling communication disruptions in distributed model predictive control

In this work, we study distributed model predictive control (DMPC) of nonlinear systems subject to communication disruptions - communication channel noise and data losses - between distributed controllers. Specifically, we focus on a DMPC architecture in which one of the distributed controllers is responsible for ensuring closed-loop stability while the rest of the distributed controllers communicate and cooperate with the stabilizing controller to further improve the closed-loop performance. To handle communication disruptions, feasibility problems are incorporated in the DMPC architecture to determine if the data transmitted through the communication channel is reliable or not. Based on the results of the feasibility problems, the transmitted information is accepted or rejected by the stabilizing MPC. In order to ensure the stability of the closed-loop system under communication disruptions, each model predictive controller utilizes a stability constraint which is based on a suitable Lyapunov-based controller. The theoretical results are demonstrated through a nonlinear chemical process example.     

Distributed model predictive control of dynamically decoupled systems with coupled cost

This paper considers the distributed model predictive control (DMPC) of systems with interacting subsystems having decoupled dynamics and constraints but coupled costs. An easily-verifiable constraint is introduced to ensure asymptotic stability of the overall system in the absence of disturbance. The constraint introduced has a parameter which allows for the performance of the DMPC system to approach that controlled by a centralized model predictive controller. When the subsystems are linear and additive disturbance is present, the added constraint ensures the state of each subsystem converges to its respective minimal disturbance invariant set. The approach is demonstrated via several numerical examples.     

Event‐triggered MPC design for distributed systems toward global performance

This paper studies the coordination control problem of stabilizing large‐scale dynamically coupled systems via a novel event‐triggered distributed model predictive control (DMPC) approach. In order to achieve global performance, certain constraints relevant to the triggering instant are imposed on the DMPC optimization problem, and triggering mechanisms are designed by taking into account coupling influences. Specifically, the triggering conditions derived from the feasibility and stability analysis are based on the local subsystem state and the information received from its neighbors. Based on these triggering mechanisms, the event‐triggered DMPC algorithm is built, and a dual‐mode strategy is adopted. As a result, the controllers solve the optimization problem and coordinate with each other asynchronously, which reduces the amount of communication and lowers the frequency of controller updates while achieving global performance. The recursive feasibility of the proposed event‐triggered DMPC algorithm is proved, and sufficient parameter conditions about the prediction horizon and the triggering threshold are established. It shows that the system state can be gradually driven into the terminal set under the proposed strategy. Finally, an academic example and a realistic simulation problem to the water level of a 4‐tank system are provided to verify the effectiveness of the proposed algorithm.     

Elman网络在Smith预测控制中的应用

Smith预测控制在实际应用中的难点在于很难得到实际系统精确的数学模型. 通过Elman网络拟合传统Smith估计器的模型误差, 并对其进行补偿. 实验结果表明, 这种基于Elman网络补偿模型的Smith预测控制充分利用了神经网络的非线性拟合能力, 只要对纯滞后环节精确建模, 就可以完全抵消纯滞后环节对控制品质及系统稳定性的不利影响. 这种方法使得Smith预测控制可以用于模型不易精确确定的系统.     

基于支持向量机的一类非线性系统预测控制

针对一类具有输出反馈耦合的离散非线性系统,将过程的非线性部分通过支持向量机转化为全局线性状态空间模型,并在目标函数中引入系统状态的变化,给出一种类似于离散PI最优调节器的新型预测控制器.该方法不需要在线辨识系统参数,因为系统的内模已转换成全局离线模型.由于引入了新的优化目标函数,该控制器的控制效果和鲁棒性优于仅考虑预测输出误差的传统预测控制器.仿真结果表明,它也优于经典离散PI最优调节器.